Shaping double descent in quantum extreme learning through model design
quant-ph
Submitted: 2026-10-05
Updated: 2026-10-05
Sources
- Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
- Early Stopping in Deep Networks: Double Descent and How to Eliminate it
- Minimum-Norm Interpolation Under Covariate Shift
- Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks
- Understanding the Role of Optimization in Double Descent
- A Primer on Quantum Machine Learning
- Generalization Error in Quantum Machine Learning in the Presence of Sampling Noise
- Double Descent in Quantum Kernel Ridge Regression
- Cautious optimism for deep parameterized quantum circuits
- Role of scrambling and noise in temporal information processing with quantum systems
- Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine
- Breaking concentration barriers for quantum extreme learning on digital quantum processors
- Fourier analysis of quantum neural network with non-linear data embedding
- The Zero Pattern of a Design Matrix Drives Multiple Descent in Over-parameterized Regression
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